Here are preliminary results of the bibliometric mapping of the 2022 Luxembourg research evaluation. Its purpose is:
The method for the research-field-mapping can be reviewed here:
The seed articles deemed representative for the active areas of research in the institution, and include authors affiliated with the institution. They can be selected in three ways:
The present analysis is based on the following seed articles:
| AU | PY | TI | JI |
|---|---|---|---|
| OUDIN A;BAUS V;BARTHELEMY V… | 2021 | PROTOCOL FOR DERIVATION OF ORGANOIDS AND PATIENT-DERIVED ORTHOTOPIC XENOGRAFTS FROM GLIOMA PATIEN… | STAR. PROTOC. |
| OIZEL K;TAIT-MULDER J;FERNA… | 2020 | FORMATE INDUCES A METABOLIC SWITCH IN NUCLEOTIDE AND ENERGY METABOLISM | CELL DEATH DIS. |
| BIOLATO AM;FILALI L;WURZER … | 2020 | ACTIN REMODELING AND VESICULAR TRAFFICKING AT THE TUMOR CELL SIDE OF THE IMMUNOLOGICAL SYNAPSE DI… | INT. REV. CELL MOL. BIOL. |
| BRETSCHER C;MARCHINI A | 2019 | H-1 PARVOVIRUS AS A CANCER-KILLING AGENT: PAST, PRESENT, AND FUTURE | VIRUSES |
| GARGIULO E;PAGGETTI J;MOUSS… | 2019 | HEMATOLOGICAL MALIGNANCY-DERIVED SMALL EXTRACELLULAR VESICLES AND TUMOR MICROENVIRONMENT: THE ART… | CELLS |
| ESKILSSON E;RØSLAND GV;SOLE… | 2018 | EGFR HETEROGENEITY AND IMPLICATIONS FOR THERAPEUTIC INTERVENTION IN GLIOBLASTOMA | NEURO-ONCOLOGY |
| JANJI B;BERCHEM G;CHOUAIB S | 2018 | TARGETING AUTOPHAGY IN THE TUMOR MICROENVIRONMENT: NEW CHALLENGES AND OPPORTUNITIES FOR REGULATIN… | FRONT. IMMUNOL. |
| NOMAN MZ;VAN MOER K;MARANI … | 2018 | CD47 IS A DIRECT TARGET OF SNAI1 AND ZEB1 AND ITS BLOCKADE ACTIVATES THE PHAGOCYTOSIS OF BREAST C… | ONCOIMMUNOLOGY |
| BAHLAWANE C;SCHMITZ M;LETEL… | 2017 | INSIGHTS INTO LIGAND STIMULATION EFFECTS ON GASTRO-INTESTINAL STROMAL TUMORS SIGNALLING | CELL. SIGNAL. |
| BOURMAUD A;GALLIEN S;DOMON B | 2016 | PARALLEL REACTION MONITORING USING QUADRUPOLE-ORBITRAP MASS SPECTROMETER: PRINCIPLE AND APPLICATIONS | PROTEOMICS |
Here, we report the results of a LDA topic-modelling (basically, clustering on words) on all title+abstract texts.
Note: While this static vies is helpful, I recommend using the interactive LDAVis version to be found under https://daniel-hain.github.io/biblio_lux_2022/output/topic_modelling/LDAviz_lih_dcr.rds/index.html#topic=1&lambda=0.60&term=. For functionality and usage, see technical description in the next tab.
Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents. Latent Dirichlet Allocation (LDA, Blei et al., 2003) is an example of topic model and is used to classify text in a document to a particular topic.
LDA is a generative probabilistic model that assumes each topic is a mixture over an underlying set of words, and each document is a mixture of over a set of topic probabilities. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions.
LDAvis is a web-based interactive visualisation of topics estimated using LDA (Sievert & Shirley, 2014). It provides a global view of the topics (and how they differ from each other), while at the same time allowing for a deep inspection of the terms most highly associated with each individual topic. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization. The visualisation has two basic pieces.
The left panel visualise the topics as circles in the two-dimensional plane whose centres are determined by computing the Jensen–Shannon divergence between topics, and then by using multidimensional scaling to project the inter-topic distances onto two dimensions. Each topic’s overall prevalence is encoded using the areas of the circles.
The right panel depicts a horizontal bar chart whose bars represent the individual terms that are the most useful for interpreting the currently selected topic on the left. A pair of overlaid bars represent both the corpus-wide frequency of a given term as well as the topic-specific frequency of the term.
The \(\lambda\) slider allows to rank the terms according to term relevance. By default, the terms of a topic are ranked in decreasing order according their topic-specific probability ( \(\lambda\) = 1 ). Moving the slider allows to adjust the rank of terms based on much discriminatory (or “relevant”) are for the specific topic. The suggested optimal value of \(\lambda\) is 0.6.
Note: This analysis refers the co-citation analysis,
where the cited references and not the original publications are the
unit of analysis. See tab Technical descriptionfor
additional explanations
In order to partition networks into components or clusters, we deploy a community detection technique based on the Lovain Algorithm (Blondel et al., 2008). The Lovain Algorithm is a heuristic method that attempts to optimize the modularity of communities within a network by maximizing within- and minimizing between-community connectivity. We identify the following communities = knowledge bases.
| name | dgr_int | dgr |
|---|---|---|
| Knowledge Base 1: KB 1: unlabeled (n = 4248, density =3.96) | ||
| RAPOSO G. STOORVOGEL W. EXTRACELLULAR VESICLES: EXOSOMES MICROVESICLES AND FRIENDS (2013) | 16828 | 17297 |
| VALADI H. EKSTROM K. BOSSIOS A. SJOSTRAND M. LEE J.J. LOTVALL J.O. EXOSOME-MEDIATED TRANSFER OF MRNAS AND MICRORNAS IS A NOVEL MECHANISM OF GENETIC… | 9851 | 10011 |
| TRAJKOVIC K. HSU C. CHIANTIA S. RAJENDRAN L. WENZEL D. WIELAND F. SCHWILLE P. SIMONS M. CERAMIDE TRIGGERS BUDDING OF EXOSOME VESICLES INTO MULTIVES… | 8374 | 8446 |
| VALADI H. EKSTRÖM K. BOSSIOS A. SJÖSTRAND M. LEE J.J. LÖTVALL J.O. EXOSOME-MEDIATED TRANSFER OF MRNAS AND MICRORNAS IS A NOVEL MECHANISM OF GENETIC… | 7018 | 7106 |
| COLOMBO M. RAPOSO G. THERY C. BIOGENESIS SECRETION AND INTERCELLULAR INTERACTIONS OF EXOSOMES AND OTHER EXTRACELLULAR VESICLES (2014) | 6392 | 6559 |
| TAYLOR D.D. GERCEL-TAYLOR C. MICRORNA SIGNATURES OF TUMOR-DERIVED EXOSOMES AS DIAGNOSTIC BIOMARKERS OF OVARIAN CANCER (2008) | 6004 | 6078 |
| RAPOSO G. NIJMAN H.W. STOORVOGEL W. LIEJENDEKKER R. HARDING C.V. MELIEF C.J. GEUZE H.J. B LYMPHOCYTES SECRETE ANTIGEN-PRESENTING VESICLES (1996) | 5666 | 5723 |
| JOHNSTONE R.M. ADAM M. HAMMOND J.R. ORR L. TURBIDE C. VESICLE FORMATION DURING RETICULOCYTE MATURATION. ASSOCIATION OF PLASMA MEMBRANE ACTIVITIES W… | 5486 | 5543 |
| ROBBINS P.D. MORELLI A.E. REGULATION OF IMMUNE RESPONSES BY EXTRACELLULAR VESICLES (2014) | 4867 | 5016 |
| HOOD J.L. SAN R.S. WICKLINE S.A. EXOSOMES RELEASED BY MELANOMA CELLS PREPARE SENTINEL LYMPH NODES FOR TUMOR METASTASIS (2011) | 4790 | 4935 |
| Knowledge Base 2: KB 2: unlabeled (n = 2439, density =2.3) | ||
| COX J. MANN M. MAXQUANT ENABLES HIGH PEPTIDE IDENTIFICATION RATES INDIVIDUALIZED P.P.B.-RANGE MASS ACCURACIES AND PROTEOME-WIDE PROTEIN QUANTIFICAT… | 9462 | 9646 |
| COX J. NEUHAUSER N. MICHALSKI A. SCHELTEMA R.A. OLSEN J.V. MANN M. ANDROMEDA: A PEPTIDE SEARCH ENGINE INTEGRATED INTO THE MAXQUANT ENVIRONMENT (2011) | 3788 | 3809 |
| COX J. HEIN M.Y. LUBER C.A. PARON I. NAGARAJ N. MANN M. ACCURATE PROTEOME-WIDE LABEL-FREE QUANTIFICATION BY DELAYED NORMALIZATION AND MAXIMAL PEPTI… | 3567 | 3595 |
| TYANOVA S. TEMU T. SINITCYN P. CARLSON A. HEIN M.Y. GEIGER T. MANN M. COX J. THE PERSEUS COMPUTATIONAL PLATFORM FOR COMPREHENSIVE ANALYSIS OF (PROTE) | 2626 | 2642 |
| RAPPSILBER J. MANN M. ISHIHAMA Y. PROTOCOL FOR MICRO-PURIFICATION ENRICHMENT PRE-FRACTIONATION AND STORAGE OF PEPTIDES FOR PROTEOMICS USING STAGETI… | 2422 | 2446 |
| PETERSON A.C. RUSSELL J.D. BAILEY D.J. WESTPHALL M.S. COON J.J. PARALLEL REACTION MONITORING FOR HIGH RESOLUTION AND HIGH MASS ACCURACY QUANTITATIV… | 2231 | 2242 |
| MACLEAN B. TOMAZELA D.M. SHULMAN N. CHAMBERS M. FINNEY G.L. FREWEN B. KERN R. MACCOSS M.J. SKYLINE: AN OPEN SOURCE DOCUMENT EDITOR FOR CREATING AND… | 2183 | 2189 |
| AEBERSOLD R. MANN M. MASS-SPECTROMETRIC EXPLORATION OF PROTEOME STRUCTURE AND FUNCTION (2016) | 1808 | 1814 |
| PICOTTI P. AEBERSOLD R. SELECTED REACTION MONITORING-BASED PROTEOMICS: WORKFLOWS POTENTIAL PITFALLS AND FUTURE DIRECTIONS (2012) | 1498 | 1507 |
| LANGE V. PICOTTI P. DOMON B. AEBERSOLD R. SELECTED REACTION MONITORING FOR QUANTITATIVE PROTEOMICS: A TUTORIAL (2008) | 1427 | 1432 |
| Knowledge Base 3: KB 3: unlabeled (n = 2220, density =1.74) | ||
| HANAHAN D. WEINBERG R.A. HALLMARKS OF CANCER: THE NEXT GENERATION (2011) | 3354 | 9690 |
| PARDOLL D.M. THE BLOCKADE OF IMMUNE CHECKPOINTS IN CANCER IMMUNOTHERAPY (2012) | 1969 | 2544 |
| SHARMA P. ALLISON J.P. THE FUTURE OF IMMUNE CHECKPOINT THERAPY (2015) | 1553 | 1704 |
| TOPALIAN S.L. DRAKE C.G. PARDOLL D.M. IMMUNE CHECKPOINT BLOCKADE: A COMMON DENOMINATOR APPROACH TO CANCER THERAPY (2015) | 832 | 1099 |
| WILSON W.R. HAY M.P. TARGETING HYPOXIA IN CANCER THERAPY (2011) | 823 | 861 |
| KEIR M.E. BUTTE M.J. FREEMAN G.J. SHARPE A.H. PD-1 AND ITS LIGANDS IN TOLERANCE AND IMMUNITY (2008) | 816 | 867 |
| BARSOUM I.B. SMALLWOOD C.A. SIEMENS D.R. GRAHAM C.H. A MECHANISM OF HYPOXIA-MEDIATED ESCAPE FROM ADAPTIVE IMMUNITY IN CANCER CELLS (2014) | 714 | 729 |
| CHEN J. JIANG C.C. JIN L. ZHANG X.D. REGULATION OF PD-L1: A NOVEL ROLE OF PRO-SURVIVAL SIGNALLING IN CANCER (2016) | 674 | 674 |
| QUAIL D.F. JOYCE J.A. MICROENVIRONMENTAL REGULATION OF TUMOR PROGRESSION AND METASTASIS (2013) | 668 | 2138 |
| ISHIDA Y. AGATA Y. SHIBAHARA K. HONJO T. INDUCED EXPRESSION OF PD-1 A NOVEL MEMBER OF THE IMMUNOGLOBULIN GENE SUPERFAMILY UPON PROGRAMMED CELL DEAT… | 537 | 554 |
| Knowledge Base 4: KB 4: unlabeled (n = 2142, density =3.64) | ||
| LEVY J.M.M. TOWERS C.G. THORBURN A. TARGETING AUTOPHAGY IN CANCER (2017) | 3527 | 3622 |
| WHITE E. DECONVOLUTING THE CONTEXT-DEPENDENT ROLE FOR AUTOPHAGY IN CANCER (2012) | 2776 | 2841 |
| WHITE E. THE ROLE FOR AUTOPHAGY IN CANCER (2015) | 2414 | 2478 |
| YUE Z. JIN S. YANG C. LEVINE A.J. HEINTZ N. BECLIN 1 AN AUTOPHAGY GENE ESSENTIAL FOR EARLY EMBRYONIC DEVELOPMENT IS A HAPLOINSUFFICIENT TUMOR SUPPR… | 1886 | 1905 |
| LIANG X.H. JACKSON S. SEAMAN M. BROWN K. KEMPKES B. HIBSHOOSH H. LEVINE B. INDUCTION OF AUTOPHAGY AND INHIBITION OF TUMORIGENESIS BY BECLIN 1 (1999) | 1811 | 1826 |
| AMARAVADI R. KIMMELMAN A.C. WHITE E. RECENT INSIGHTS INTO THE FUNCTION OF AUTOPHAGY IN CANCER (2016) | 1786 | 1804 |
| KIMMELMAN A.C. WHITE E. AUTOPHAGY AND TUMOR METABOLISM (2017) | 1768 | 1791 |
| KIM J. KUNDU M. VIOLLET B. GUAN K.L. AMPK AND MTOR REGULATE AUTOPHAGY THROUGH DIRECT PHOSPHORYLATION OF ULK1 (2011) | 1762 | 1786 |
| MIZUSHIMA N. KOMATSU M. AUTOPHAGY: RENOVATION OF CELLS AND TISSUES (2011) | 1723 | 1748 |
| MIZUSHIMA N. YOSHIMORI T. OHSUMI Y. THE ROLE OF ATG PROTEINS IN AUTOPHAGOSOME FORMATION (2011) | 1570 | 1583 |
| Knowledge Base 5: KB 5: unlabeled (n = 1498, density =3.18) | ||
| VERHAAK R.G. HOADLEY K.A. PURDOM E. WANG V. QI Y. WILKERSON M.D. MILLER C.R. MESIROV J.P. INTEGRATED GENOMIC ANALYSIS IDENTIFIES CLINICALLY RELEVAN… | 2238 | 2327 |
| COMPREHENSIVE GENOMIC CHARACTERIZATION DEFINES HUMAN GLIOBLASTOMA GENES AND CORE PATHWAYS (2008) | 2146 | 2324 |
| LOUIS D.N. PERRY A. REIFENBERGER G. VON DEIMLING A. FIGARELLA-BRANGER D. CAVENEE W.K. OHGAKI H. ELLISON D.W. THE 2016 WORLD HEALTH ORGANIZATION CLA… | 1895 | 2030 |
| PATEL A.P. TIROSH I. TROMBETTA J.J. SHALEK A.K. GILLESPIE S.M. WAKIMOTO H. CAHILL D.P. MARTUZA R.L. SINGLE-CELL RNA-SEQ HIGHLIGHTS INTRATUMORAL HET… | 1589 | 1703 |
| BRENNAN C.W. THE SOMATIC GENOMIC LANDSCAPE OF GLIOBLASTOMA (2013) | 1338 | 1447 |
| BRENNAN C.W. VERHAAK R.G. MCKENNA A. CAMPOS B. NOUSHMEHR H. SALAMA S.R. ZHENG S. BERMAN S.H. THE SOMATIC GENOMIC LANDSCAPE OF GLIOBLASTOMA (2013) | 1211 | 1277 |
| STUPP R. MASON W.P. VAN DEN BENT M.J. WELLER M. FISHER B. TAPHOORN M.J. BELANGER K. BOGDAHN U. RADIOTHERAPY PLUS CONCOMITANT AND ADJUVANT TEMOZOLOM… | 1189 | 1282 |
| VERHAAK R.G. INTEGRATED GENOMIC ANALYSIS IDENTIFIES CLINICALLY RELEVANT SUBTYPES OF GLIOBLASTOMA CHARACTERIZED BY ABNORMALITIES IN PDGFRA IDH1 EGFR… | 1147 | 1234 |
| PATEL A.P. SINGLE-CELL RNA-SEQ HIGHLIGHTS INTRATUMORAL HETEROGENEITY IN PRIMARY GLIOBLASTOMA (2014) | 1141 | 1215 |
| BAO S. WU Q. MCLENDON R.E. HAO Y. SHI Q. HJELMELAND A.B. DEWHIRST M.W. RICH J.N. GLIOMA STEM CELLS PROMOTE RADIORESISTANCE BY PREFERENTIAL ACTIVATI… | 1113 | 1151 |
| Knowledge Base 6: KB 6: unlabeled (n = 1331, density =3.32) | ||
| KAUFMAN H.L. KOHLHAPP F.J. ZLOZA A. ONCOLYTIC VIRUSES: A NEW CLASS OF IMMUNOTHERAPY DRUGS (2015) | 3039 | 3383 |
| RUSSELL S.J. PENG K.W. BELL J.C. ONCOLYTIC VIROTHERAPY (2012) | 1889 | 2014 |
| LICHTY B.D. BREITBACH C.J. STOJDL D.F. BELL J.C. GOING VIRAL WITH CANCER IMMUNOTHERAPY (2014) | 1530 | 1712 |
| BOMMAREDDY P.K. SHETTIGAR M. KAUFMAN H.L. INTEGRATING ONCOLYTIC VIRUSES IN COMBINATION CANCER IMMUNOTHERAPY (2018) | 1201 | 1370 |
| KELLY E. RUSSELL S.J. HISTORY OF ONCOLYTIC VIRUSES: GENESIS TO GENETIC ENGINEERING (2007) | 1200 | 1271 |
| ANDTBACKA R.H. KAUFMAN H.L. COLLICHIO F. AMATRUDA T. SENZER N. CHESNEY J. DELMAN K.A. AGARWALA S.S. TALIMOGENE LAHERPAREPVEC IMPROVES DURABLE RESPO… | 1102 | 1182 |
| RIBAS A. DUMMER R. PUZANOV I. VANDERWALDE A. ANDTBACKA R.H.I. MICHIELIN O. OLSZANSKI A.J. FERNANDEZ E. ONCOLYTIC VIROTHERAPY PROMOTES INTRATUMORAL … | 998 | 1095 |
| ZAMARIN D. HOLMGAARD R.B. SUBUDHI S.K. PARK J.S. MANSOUR M. PALESE P. MERGHOUB T. ALLISON J.P. LOCALIZED ONCOLYTIC VIROTHERAPY OVERCOMES SYSTEMIC T… | 888 | 954 |
| KAUFMAN H.L. KIM D.W. DERAFFELE G. MITCHAM J. COFFIN R.S. KIM-SCHULZE S. LOCAL AND DISTANT IMMUNITY INDUCED BY INTRALESIONAL VACCINATION WITH AN ON… | 765 | 798 |
| LIU Z. RAVINDRANATHAN R. KALINSKI P. GUO Z.S. BARTLETT D.L. RATIONAL COMBINATION OF ONCOLYTIC VACCINIA VIRUS AND PD-L1 BLOCKADE WORKS SYNERGISTICAL… | 732 | 823 |
In a co-cittion network, the strength of the relationship between a reference pair \(m\) and \(n\) (\(s_{m,n}^{coc}\)) is expressed by the number of publications \(C\) which are jointly citing reference \(m\) and \(n\).
\[s_{m,n}^{coc} = \sum_i c_{i,m} c_{i,n}\]
The intuition here is that references which are frequently cited together are likely to share commonalities in theory, topic, methodology, or context. It can be interpreted as a measure of similarity as evaluated by other researchers that decide to jointly cite both references. Because the publication process is time-consuming, co-citation is a backward-looking measure, which is appropriate to map the relationship between core literature of a field.
This is arguably the more interesting part. Here, we identify the
literature’s current knowledge frontier by carrying out a bibliographic
coupling analysis of the publications in our corpus. This measure uses
bibliographical information of publications to establish a similarity
relationship between them. Again, method details to be found in the tab
Technical description. As you will see, we identify the
main research area, but also a set of adjacent research areas with some
theoretical/methodological/application overlap.
To identify communities in the field’s knowledge frontier (labeled research areas) we again use the Lovain Algorithm (Blondel et al., 2008). We identify the following communities = research areas.
| label | AU | PY | TI | dgr_int | TC | TC_year |
|---|---|---|---|---|---|---|
| Research Area 1: RA 1: unlabeled (n = 1782, density =0.26) | ||||||
| RA 1: unlabeled | VAN NIEL G;D’ANGELO G;… | 2018 | SHEDDING LIGHT ON THE CELL BIOLOGY OF EXTRACELLULAR VESICLES | 10.77 | 2601 | 650.25 |
| RA 1: unlabeled | BECKER A;THAKUR BK;WEI… | 2016 | EXTRACELLULAR VESICLES IN CANCER: CELL-TO-CELL MEDIATORS OF METASTASIS | 19.93 | 910 | 151.67 |
| RA 1: unlabeled | KALLURI R | 2016 | THE BIOLOGY AND FUNCTION OF EXOSOMES IN CANCER | 18.12 | 899 | 149.83 |
| RA 1: unlabeled | HESSVIK NP;LLORENTE A | 2018 | CURRENT KNOWLEDGE ON EXOSOME BIOGENESIS AND RELEASE | 14.28 | 1018 | 254.50 |
| RA 1: unlabeled | KALLURI R;LEBLEU VS | 2020 | THE BIOLOGY, FUNCTION, AND BIOMEDICAL APPLICATIONS OF EXOSOMES | 7.88 | 1596 | 798.00 |
| RA 1: unlabeled | KOWAL J;ARRAS G;COLOMB… | 2016 | PROTEOMIC COMPARISON DEFINES NOVEL MARKERS TO CHARACTERIZE HETEROGENEOUS POPULATIONS OF EXTRACELLULAR VESICLE SUBTYPES | 6.84 | 1696 | 282.67 |
| RA 1: unlabeled | LI P;KASLAN M;LEE SH;Y… | 2017 | PROGRESS IN EXOSOME ISOLATION TECHNIQUES | 12.15 | 828 | 165.60 |
| RA 1: unlabeled | MATHIEU M;MARTIN-JAULA… | 2019 | SPECIFICITIES OF SECRETION AND UPTAKE OF EXOSOMES AND OTHER EXTRACELLULAR VESICLES FOR CELL-TO-CELL COMMUNICATION | 7.43 | 1262 | 420.67 |
| RA 1: unlabeled | WHITESIDE TL | 2016 | TUMOR-DERIVED EXOSOMES AND THEIR ROLE IN CANCER PROGRESSION | 20.74 | 372 | 62.00 |
| RA 1: unlabeled | ABELS ER;BREAKEFIELD XO | 2016 | INTRODUCTION TO EXTRACELLULAR VESICLES: BIOGENESIS, RNA CARGO SELECTION, CONTENT, RELEASE, AND UPTAKE | 9.58 | 668 | 111.33 |
| Research Area 2: RA 2: unlabeled (n = 1319, density =0.12) | ||||||
| RA 2: unlabeled | MANTOVANI A;MARCHESI F… | 2017 | TUMOUR-ASSOCIATED MACROPHAGES AS TREATMENT TARGETS IN ONCOLOGY | 2.17 | 1598 | 319.60 |
| RA 2: unlabeled | FARKONA S;DIAMANDIS EP… | 2016 | CANCER IMMUNOTHERAPY: THE BEGINNING OF THE END OF CANCER? | 3.96 | 628 | 104.67 |
| RA 2: unlabeled | HARATANI K;HAYASHI H;C… | 2018 | ASSOCIATION OF IMMUNE-RELATED ADVERSE EVENTS WITH NIVOLUMAB EFFICACY IN NON-SMALL CELL LUNG CANCER | 4.63 | 513 | 128.25 |
| RA 2: unlabeled | SUN C;MEZZADRA R;SCHUM… | 2018 | REGULATION AND FUNCTION OF THE PD-L1 CHECKPOINT | 2.77 | 758 | 189.50 |
| RA 2: unlabeled | HE C;DUAN X;GUO N;CHAN… | 2016 | CORE-SHELL NANOSCALE COORDINATION POLYMERS COMBINE CHEMOTHERAPY AND PHOTODYNAMIC THERAPY TO POTENTIATE CHECKPOINT BLOCKADE… | 3.50 | 472 | 78.67 |
| RA 2: unlabeled | PFIRSCHKE C;ENGBLOM C;… | 2016 | IMMUNOGENIC CHEMOTHERAPY SENSITIZES TUMORS TO CHECKPOINT BLOCKADE THERAPY | 2.81 | 524 | 87.33 |
| RA 2: unlabeled | HUGO W;ZARETSKY JM;SUN… | 2016 | GENOMIC AND TRANSCRIPTOMIC FEATURES OF RESPONSE TO ANTI-PD-1 THERAPY IN METASTATIC MELANOMA | 0.97 | 1482 | 247.00 |
| RA 2: unlabeled | SMYTH MJ;NGIOW SF;RIBA… | 2016 | COMBINATION CANCER IMMUNOTHERAPIES TAILORED TO THE TUMOUR MICROENVIRONMENT | 2.47 | 556 | 92.67 |
| RA 2: unlabeled | YANG W;BAI Y;XIONG Y;Z… | 2016 | POTENTIATING THE ANTITUMOUR RESPONSE OF CD8+ T CELLS BY MODULATING CHOLESTEROL METABOLISM | 2.78 | 388 | 64.67 |
| RA 2: unlabeled | ADVANI R;FLINN I;POPPL… | 2018 | CD47 BLOCKADE BY HU5F9-G4 AND RITUXIMAB IN NON-HODGKIN’S LYMPHOMA | 2.34 | 456 | 114.00 |
| Research Area 3: RA 3: unlabeled (n = 1032, density =0.97) | ||||||
| RA 3: unlabeled | GEIGER R;RIECKMANN JC;… | 2016 | L-ARGININE MODULATES T CELL METABOLISM AND ENHANCES SURVIVAL AND ANTI-TUMOR ACTIVITY | 12.42 | 622 | 103.67 |
| RA 3: unlabeled | GEYER PE;KULAK NA;PICH… | 2016 | PLASMA PROTEOME PROFILING TO ASSESS HUMAN HEALTH AND DISEASE | 18.44 | 334 | 55.67 |
| RA 3: unlabeled | MEIER F;BRUNNER A-D;KO… | 2018 | ONLINE PARALLEL ACCUMULATION–SERIAL FRAGMENTATION (PASEF) WITH A NOVEL TRAPPED ION MOBILITY MASS SPECTROMETER | 16.00 | 253 | 63.25 |
| RA 3: unlabeled | WHITHAM M;PARKER BL;FR… | 2018 | EXTRACELLULAR VESICLES PROVIDE A MEANS FOR TISSUE CROSSTALK DURING EXERCISE | 15.54 | 249 | 62.25 |
| RA 3: unlabeled | STEGER M;DIEZ F;DHEKNE… | 2017 | SYSTEMATIC PROTEOMIC ANALYSIS OF LRRK2-MEDIATED RAB GTPASE PHOSPHORYLATION ESTABLISHES A CONNECTION TO CILIOGENESIS | 18.79 | 184 | 36.80 |
| RA 3: unlabeled | ITZHAK DN;TYANOVA S;CO… | 2016 | GLOBAL, QUANTITATIVE AND DYNAMIC MAPPING OF PROTEIN SUBCELLULAR LOCALIZATION | 11.41 | 272 | 45.33 |
| RA 3: unlabeled | SCHROEDER BO;BIRCHENOU… | 2018 | BIFIDOBACTERIA OR FIBER PROTECTS AGAINST DIET-INDUCED MICROBIOTA-MEDIATED COLONIC MUCUS DETERIORATION | 10.42 | 287 | 71.75 |
| RA 3: unlabeled | GRASSL N;KULAK NA;PICH… | 2016 | ULTRA-DEEP AND QUANTITATIVE SALIVA PROTEOME REVEALS DYNAMICS OF THE ORAL MICROBIOME | 23.44 | 122 | 20.33 |
| RA 3: unlabeled | BACHE N;GEYER PE;BEKKE… | 2018 | A NOVEL LC SYSTEM EMBEDS ANALYTES IN PRE-FORMED GRADIENTS FOR RAPID, ULTRA-ROBUST PROTEOMICS | 23.93 | 110 | 27.50 |
| RA 3: unlabeled | BONFIGLIO JJ;FONTANA P… | 2017 | SERINE ADP-RIBOSYLATION DEPENDS ON HPF1 | 16.24 | 157 | 31.40 |
| Research Area 4: RA 4: unlabeled (n = 969, density =0.22) | ||||||
| RA 4: unlabeled | TYANOVA S;TEMU T;COX J | 2016 | THE MAXQUANT COMPUTATIONAL PLATFORM FOR MASS SPECTROMETRY-BASED SHOTGUN PROTEOMICS | 8.34 | 1502 | 250.33 |
| RA 4: unlabeled | TYANOVA S;TEMU T;SINIT… | 2016 | THE PERSEUS COMPUTATIONAL PLATFORM FOR COMPREHENSIVE ANALYSIS OF (PROTE)OMICS DATA | 3.16 | 2960 | 493.33 |
| RA 4: unlabeled | AEBERSOLD R;MANN M | 2016 | MASS-SPECTROMETRIC EXPLORATION OF PROTEOME STRUCTURE AND FUNCTION | 7.56 | 988 | 164.67 |
| RA 4: unlabeled | VIZCAÍNO JA;CSORDAS A;… | 2016 | 2016 UPDATE OF THE PRIDE DATABASE AND ITS RELATED TOOLS | 1.73 | 2696 | 449.33 |
| RA 4: unlabeled | LUDWIG C;GILLET L;ROSE… | 2018 | DATA-INDEPENDENT ACQUISITION-BASED SWATH-MS FOR QUANTITATIVE PROTEOMICS: A TUTORIAL | 6.65 | 353 | 88.25 |
| RA 4: unlabeled | KLAEGER S;HEINZLMEIR S… | 2017 | THE TARGET LANDSCAPE OF CLINICAL KINASE DRUGS | 5.68 | 370 | 74.00 |
| RA 4: unlabeled | HOLDT LM;STAHRINGER A;… | 2016 | CIRCULAR NON-CODING RNA ANRIL MODULATES RIBOSOMAL RNA MATURATION AND ATHEROSCLEROSIS IN HUMANS | 3.14 | 614 | 102.33 |
| RA 4: unlabeled | MEIER F;GEYER PE;VIRRE… | 2018 | BOXCAR ACQUISITION METHOD ENABLES SINGLE-SHOT PROTEOMICS AT A DEPTH OF 10,000 PROTEINS IN 100 MINUTES | 6.06 | 191 | 47.75 |
| RA 4: unlabeled | GEYER PE;HOLDT LM;TEUP… | 2017 | REVISITING BIOMARKER DISCOVERY BY PLASMA PROTEOMICS | 3.61 | 318 | 63.60 |
| RA 4: unlabeled | RIECKMANN JC;GEIGER R;… | 2017 | SOCIAL NETWORK ARCHITECTURE OF HUMAN IMMUNE CELLS UNVEILED BY QUANTITATIVE PROTEOMICS | 6.55 | 171 | 34.20 |
| Research Area 5: RA 5: unlabeled (n = 850, density =0.21) | ||||||
| RA 5: unlabeled | LEVY JMM;TOWERS CG;THO… | 2017 | TARGETING AUTOPHAGY IN CANCER | 4.48 | 1171 | 234.20 |
| RA 5: unlabeled | KIMMELMAN AC;WHITE E | 2017 | AUTOPHAGY AND TUMOR METABOLISM | 7.85 | 441 | 88.20 |
| RA 5: unlabeled | AMARAVADI R;KIMMELMAN … | 2016 | RECENT INSIGHTS INTO THE FUNCTION OF AUTOPHAGY IN CANCER | 6.93 | 447 | 74.50 |
| RA 5: unlabeled | DIKIC I;ELAZAR Z | 2018 | MECHANISM AND MEDICAL IMPLICATIONS OF MAMMALIAN AUTOPHAGY | 2.29 | 1081 | 270.25 |
| RA 5: unlabeled | YUN CW;LEE SH | 2018 | THE ROLES OF AUTOPHAGY IN CANCER | 6.93 | 332 | 83.00 |
| RA 5: unlabeled | LEVINE B;KROEMER G | 2019 | BIOLOGICAL FUNCTIONS OF AUTOPHAGY GENES: A DISEASE PERSPECTIVE | 2.52 | 850 | 283.33 |
| RA 5: unlabeled | SINGH SS;VATS S;CHIA A… | 2018 | DUAL ROLE OF AUTOPHAGY IN HALLMARKS OF CANCER | 4.99 | 273 | 68.25 |
| RA 5: unlabeled | MAUTHE M;ORHON I;ROCCH… | 2018 | CHLOROQUINE INHIBITS AUTOPHAGIC FLUX BY DECREASING AUTOPHAGOSOME-LYSOSOME FUSION | 1.60 | 758 | 189.50 |
| RA 5: unlabeled | AMARAVADI RK;KIMMELMAN… | 2019 | TARGETING AUTOPHAGY IN CANCER: RECENT ADVANCES AND FUTURE DIRECTIONS | 4.17 | 290 | 96.67 |
| RA 5: unlabeled | ONORATI AV;DYCZYNSKI M… | 2018 | TARGETING AUTOPHAGY IN CANCER | 4.25 | 260 | 65.00 |
| Research Area 6: RA 6: unlabeled (n = 761, density =0.21) | ||||||
| RA 6: unlabeled | WANG J;CAZZATO E;LADEW… | 2016 | CLONAL EVOLUTION OF GLIOBLASTOMA UNDER THERAPY | 5.49 | 373 | 62.17 |
| RA 6: unlabeled | LAN X;JÖRG DJ;CAVALLI … | 2017 | FATE MAPPING OF HUMAN GLIOBLASTOMA REVEALS AN INVARIANT STEM CELL HIERARCHY | 5.26 | 185 | 37.00 |
| RA 6: unlabeled | SIDDIQUI I;SCHAEUBLE K… | 2019 | INTRATUMORAL TCF1 + PD-1 + CD8 + T CELLS WITH STEM-LIKE PROPERTIES PROMOTE TUMOR CONTROL IN RESPONSE TO VACCINATION AND CH… | 2.16 | 435 | 145.00 |
| RA 6: unlabeled | BARTHEL FP;JOHNSON KC;… | 2019 | LONGITUDINAL MOLECULAR TRAJECTORIES OF DIFFUSE GLIOMA IN ADULTS | 5.91 | 136 | 45.33 |
| RA 6: unlabeled | REIFENBERGER G;WIRSCHI… | 2017 | ADVANCES IN THE MOLECULAR GENETICS OF GLIOMAS-IMPLICATIONS FOR CLASSIFICATION AND THERAPY | 2.51 | 310 | 62.00 |
| RA 6: unlabeled | CAPPER D;JONES DTW;SIL… | 2018 | DNA METHYLATION-BASED CLASSIFICATION OF CENTRAL NERVOUS SYSTEM TUMOURS | 0.78 | 976 | 244.00 |
| RA 6: unlabeled | OSUKA S;VAN MEIR EG | 2017 | OVERCOMING THERAPEUTIC RESISTANCE IN GLIOBLASTOMA: THE WAY FORWARD | 3.39 | 223 | 44.60 |
| RA 6: unlabeled | ZHAO J;CHEN AX;GARTREL… | 2019 | IMMUNE AND GENOMIC CORRELATES OF RESPONSE TO ANTI-PD-1 IMMUNOTHERAPY IN GLIOBLASTOMA | 2.44 | 302 | 100.67 |
| RA 6: unlabeled | LEE J-K;WANG J;SA JK;L… | 2017 | SPATIOTEMPORAL GENOMIC ARCHITECTURE INFORMS PRECISION ONCOLOGY IN GLIOBLASTOMA | 4.80 | 135 | 27.00 |
| RA 6: unlabeled | MILLER AM;SHAH RH;PENT… | 2019 | TRACKING TUMOUR EVOLUTION IN GLIOMA THROUGH LIQUID BIOPSIES OF CEREBROSPINAL FLUID | 3.35 | 192 | 64.00 |
| Research Area 7: RA 7: unlabeled (n = 690, density =0.19) | ||||||
| RA 7: unlabeled | DONGRE A;WEINBERG RA | 2019 | NEW INSIGHTS INTO THE MECHANISMS OF EPITHELIAL–MESENCHYMAL TRANSITION AND IMPLICATIONS FOR CANCER | 3.64 | 1096 | 365.33 |
| RA 7: unlabeled | BRABLETZ T;KALLURI R;N… | 2018 | EMT IN CANCER | 3.38 | 947 | 236.75 |
| RA 7: unlabeled | PURAM SV;TIROSH I;PARI… | 2017 | SINGLE-CELL TRANSCRIPTOMIC ANALYSIS OF PRIMARY AND METASTATIC TUMOR ECOSYSTEMS IN HEAD AND NECK CANCER | 2.89 | 801 | 160.20 |
| RA 7: unlabeled | CHAFFER CL;SAN JUAN BP… | 2016 | EMT, CELL PLASTICITY AND METASTASIS | 4.57 | 455 | 75.83 |
| RA 7: unlabeled | BATLLE E;CLEVERS H | 2017 | CANCER STEM CELLS REVISITED | 1.70 | 1104 | 220.80 |
| RA 7: unlabeled | MITTAL V | 2018 | EPITHELIAL MESENCHYMAL TRANSITION IN TUMOR METASTASIS | 4.31 | 420 | 105.00 |
| RA 7: unlabeled | PASTUSHENKO I;BRISEBAR… | 2018 | IDENTIFICATION OF THE TUMOUR TRANSITION STATES OCCURRING DURING EMT | 2.54 | 621 | 155.25 |
| RA 7: unlabeled | YANG J;ANTIN P;BERX G;… | 2020 | GUIDELINES AND DEFINITIONS FOR RESEARCH ON EPITHELIAL–MESENCHYMAL TRANSITION | 3.21 | 450 | 225.00 |
| RA 7: unlabeled | AIELLO NM;MADDIPATI R;… | 2018 | EMT SUBTYPE INFLUENCES EPITHELIAL PLASTICITY AND MODE OF CELL MIGRATION | 4.70 | 267 | 66.75 |
| RA 7: unlabeled | KREBS AM;MITSCHKE J;LO… | 2017 | THE EMT-ACTIVATOR ZEB1 IS A KEY FACTOR FOR CELL PLASTICITY AND PROMOTES METASTASIS IN PANCREATIC CANCER | 2.31 | 503 | 100.60 |
| Research Area 8: RA 8: unlabeled (n = 564, density =0.3) | ||||||
| RA 8: unlabeled | CECCARELLI M;BARTHEL F… | 2016 | MOLECULAR PROFILING REVEALS BIOLOGICALLY DISCRETE SUBSETS AND PATHWAYS OF PROGRESSION IN DIFFUSE GLIOMA | 4.38 | 1091 | 181.83 |
| RA 8: unlabeled | WANG Q;HU B;HU X;KIM H… | 2017 | TUMOR EVOLUTION OF GLIOMA-INTRINSIC GENE EXPRESSION SUBTYPES ASSOCIATES WITH IMMUNOLOGICAL CHANGES IN THE MICROENVIRONMENT | 6.15 | 640 | 128.00 |
| RA 8: unlabeled | LIU J;LICHTENBERG T;HO… | 2018 | AN INTEGRATED TCGA PAN-CANCER CLINICAL DATA RESOURCE TO DRIVE HIGH-QUALITY SURVIVAL OUTCOME ANALYTICS | 2.94 | 982 | 245.50 |
| RA 8: unlabeled | NEFTEL C;LAFFY J;FILBI… | 2019 | AN INTEGRATIVE MODEL OF CELLULAR STATES, PLASTICITY, AND GENETICS FOR GLIOBLASTOMA | 5.35 | 515 | 171.67 |
| RA 8: unlabeled | SEGERMAN A;NIKLASSON M… | 2016 | CLONAL VARIATION IN DRUG AND RADIATION RESPONSE AMONG GLIOMA-INITIATING CELLS IS LINKED TO PRONEURAL-MESENCHYMAL TRANSITION | 8.03 | 105 | 17.50 |
| RA 8: unlabeled | HU B;WANG Q;WANG YA;HU… | 2016 | EPIGENETIC ACTIVATION OF WNT5A DRIVES GLIOBLASTOMA STEM CELL DIFFERENTIATION AND INVASIVE GROWTH | 5.35 | 147 | 24.50 |
| RA 8: unlabeled | DARMANIS S;SLOAN SA;CR… | 2017 | SINGLE-CELL RNA-SEQ ANALYSIS OF INFILTRATING NEOPLASTIC CELLS AT THE MIGRATING FRONT OF HUMAN GLIOBLASTOMA | 2.10 | 331 | 66.20 |
| RA 8: unlabeled | BOWMAN RL;KLEMM F;AKKA… | 2016 | MACROPHAGE ONTOGENY UNDERLIES DIFFERENCES IN TUMOR-SPECIFIC EDUCATION IN BRAIN MALIGNANCIES | 2.43 | 274 | 45.67 |
| RA 8: unlabeled | JACOB F;SALINAS RD;ZHA… | 2020 | A PATIENT-DERIVED GLIOBLASTOMA ORGANOID MODEL AND BIOBANK RECAPITULATES INTER- AND INTRA-TUMORAL HETEROGENEITY | 2.94 | 221 | 110.50 |
| RA 8: unlabeled | LENTING K;VERHAAK R;TE… | 2017 | GLIOMA: EXPERIMENTAL MODELS AND REALITY | 3.71 | 159 | 31.80 |
In a bibliographic coupling network, the coupling-strength between publications is determined by the number of commonly cited references they share, assuming a common pool of references to indicate similarity in context, methods, or theory. Formally, the strength of the relationship between a publication pair \(i\) and \(j\) (\(s_{i,j}^{bib}\)) is expressed by the number of commonly cited references.
\[s_{i,j}^{bib} = \sum_m c_{i,m} c_{j,m}\]
Since our corpus contains publications which differ strongly in terms of the number of cited references, we normalize the coupling strength by the Jaccard similarity coefficient. Here, we weight the intercept of two publications’ bibliography (shared refeences) by their union (number of all references cited by either \(i\) or \(j\)). It is bounded between zero and one, where one indicates the two publications to have an identical bibliography, and zero that they do not share any cited reference. Thereby, we prevent publications from having high coupling strength due to a large bibliography (e.g., literature surveys).
\[S_{i,j}^{jac-bib} =\frac{C(i \cap j)}{C(i \cup j)} = \frac{s_{i,j}^{bib}}{c_i + c_j - s_{i,j}^{bib}}\]
More recent articles have a higher pool of possible references to co-cite to, hence they are more likely to be coupled. Consequently, bibliographic coupling represents a forward looking measure, and the method of choice to identify the current knowledge frontier at the point of analysis.
All results are preliminary so far…